Source URL: https://simonwillison.net/2025/Apr/26/elliot-higgins/#atom-everything
Source: Simon Willison’s Weblog
Title: Quoting Eliot Higgins, Bellingcat
Feedly Summary: We’ve been seeing if the latest versions of LLMs are any better at geolocating and chronolocating images, and they’ve improved dramatically since we last tested them in 2023. […]
Before anyone worries about it taking our job, I see it more as the difference between a hand whisk and an electric whisk, just the same job done quicker, and either way you’ve got to check if your peaks are stiff at the end of it.
— Eliot Higgins, Bellingcat, on LLMs for guessing locations from photos
Tags: vision-llms, bellingcat, data-journalism, llms, ai-ethics, ai, generative-ai
AI Summary and Description: Yes
Summary: The focus of the text is on the improved capabilities of large language models (LLMs) in geolocating and chronolocating images, highlighting advancements in AI technology. Eliot Higgins emphasizes that while the technology streamlines the process, human oversight remains essential.
Detailed Description: The provided text discusses advancements in large language models, particularly in their ability to analyze visual data for geolocation and chronological information extraction. Here are the key points of significance:
– **Improved LLM Capabilities**: The text notes significant enhancements in LLMs for accurately pinpointing geographical and temporal data from images, suggesting a critical evolution in AI technology since 2023.
– **Comparison to Traditional Tools**: Eliot Higgins likens the advancements in LLMs to the transition from a manual hand whisk to an electric whisk, illustrating how technology can expedite tasks without fully replacing the need for human involvement.
– **Importance of Human Oversight**: Despite the advancements, Higgins underscores the necessity of human checking, cautioning that AI can assist but should not entirely supplant human judgment in sensitive tasks like image analysis.
Implications for Security and Compliance Professionals:
– **AI Utilization in Security**: The improvements in LLM technology can be leveraged in various security applications, such as verifying the authenticity of images and aiding in investigations.
– **Ethical Considerations**: With the capabilities of LLMs growing, professionals must consider the ethical implications of AI usage in sensitive areas like surveillance and data collection.
– **Human-AI Collaboration**: The need for human oversight emphasizes the concept of collaborative AI systems where technology assists but does not replace critical human decision-making—an essential aspect for compliance in security practices.
In essence, the advancements in LLMs present significant opportunities and challenges in AI ethics, security, and the interplay between human judgment and artificial intelligence capabilities.